Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategie...Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.展开更多
Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biase...Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.展开更多
Objectives:To identify the subgroups of self-reported outcomes and associated factors among breast cancer patients undergoing surgery and chemotherapy.Methods:A cross-sectional study was conducted between January and ...Objectives:To identify the subgroups of self-reported outcomes and associated factors among breast cancer patients undergoing surgery and chemotherapy.Methods:A cross-sectional study was conducted between January and November 2021.We recruited patients from two tertiary hospitals in Shanghai,China,using convenience sampling during their hospitalization.Patients were assessed using a questionnaire that included sociodemographic and clinical characteristics,the Patient Reported Outcomes Measurement Information System profile-29(PROMIS-29),and the PROMIS-cognitive function short form 4a.Latent class analysis was performed to examine possible classes regarding self-reported outcomes.Multiple logistic regression analysis was used to determine the associated factors.Analysis of variance(ANOVA)was conducted for symptoms across the different classes.Results:A total of 640 patients participated in this study.The findings revealed three subgroups in terms of self-reported outcomes among breast cancer patients undergoing surgery and chemotherapy:low physical-social-cognitive function,high physical-low cognitive function,and high physical-socialcognitive function.Multivariable logistic regression analysis showed that age(≥60 years old),menopause,the third chemotherapy cycle,undergoing simple mastectomy and breast reconstruction,duration of disease 3-12 months,stageⅢ/Ⅳcancer,and severe pain were associated factors of the functional decline groups.Besides,significant differences in depression and sleep disorders were observed among the three groups.Conclusions:Breast cancer patients receiving surgery and chemotherapy can be divided into three subgroups.Aging,menopause,chemotherapy cycle,surgery type,duration and stage of disease,and severe pain affected the functional decline groups.Consequently,healthcare professionals should make tailored interventions to address the specific functional rehabilitation and symptom relief needs.展开更多
Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH pati...Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.展开更多
In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to id...In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.展开更多
Critical transitions in ecosystems may imply risks of unexpected collapse under climate changes,especially vegetation often responds sensitively to climate change.The type of vegetation ecosystem states could present ...Critical transitions in ecosystems may imply risks of unexpected collapse under climate changes,especially vegetation often responds sensitively to climate change.The type of vegetation ecosystem states could present alternative stable states,and its type could signal the critical transitions at tipping points because of changed climate or other drivers.This study analyzed the distribution of four key vegetation ecosystem types:desert,grassland,forest-steppe ecotone and forest,in Tibetan Plateau in China,using the latent class analysis method based on remote sensing data and climate data.This study analyzed the impacts of three key climate factors,precipitation,temperature,and sunshine duration,on the vegetation states,and calculated the critical transition tipping point of potential changes in vegetation type in Tibetan Plateau with the logistic regression model.The studied results showed that climatic factors greatly affect the vegetation states and vulnerability of the Tibetan Plateau.In comparison with temperature and sunshine duration,precipitation shows more obvious impact on differentiations of the vegetations status probability.The precipitation tipping point for desert and grassland transition is averagely 48.0 mm/month,70.7 mm/month for grassland and forest-steppe ecotone,and 115.0 mm/month for forest-steppe ecotone and forest.Both temperature and sunshine duration only show different probability change between vegetation and non-vegetation type,but produce opposite impacts.In Tibetan Plateau,the transition tipping points of vegetation and nonvegetation are about 12.1°C/month and 173.6 h/month for the temperature and sunshine duration,respectively.Further,vulnerability maps calculated with the logistic regression results presented the distribution of vulnerability of Tibetan Plateau key ecosystems.The vulnerability of the typical ecosystems in the Tibetan Plateau is low in the southeast and is high in the northwest.The meteorological factors affect tree cover as well as the transition probability that occurs in different vegetation states.This study can provide reference for local government agencies to formulate regional development strategies and environmental protection laws and regulations.展开更多
Purpose:The purposes of this study were to examine the trajectories of athlete burnout across a 2-month period characterized by high physical,psychological,and social demands to explore(1)whether several subgroups of ...Purpose:The purposes of this study were to examine the trajectories of athlete burnout across a 2-month period characterized by high physical,psychological,and social demands to explore(1)whether several subgroups of athletes representing distinct burnout trajectories emerged from the analyses and(2)whether athlete burnout symptoms(reduced accomplishment,sport devaluation,and exhaustion)developed in tandem or whether some burnout dimensions predicted downstream changes in other dimensions(causal ordering model).Methods:One hundred and fifty-nine table tennis players in intensive training centers completed a self-reported athlete burnout measure across 3 time points within a 2-month period characterized by high demands.Data were analyzed through latent class growth analysis.Results:Results of latent class growth analysis showed 3 distinct trajectories for each athlete burnout dimension,indicating not only linear or quadratic change but also stability in longitudinal athlete burnout perceptions.Results also suggested that the 3 dimensions of athlete burnout did not develop in tandem.Rather,the likelihood of belonging to particular emerging trajectories of sport devaluation and physical/emotional exhaustion was significantly influenced by the athletes’perception of reduced accomplishment assessed at Time 1.Thus,reduced accomplishment predicted downstream changes in the 2 other athlete burnout dimensions.Conclusion:As a whole,these results highlighted that the multinomial heterogeneity in longitudinal athlete burnout symptoms needs to be accounted for in future research.展开更多
Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software pack...Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.展开更多
Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places.Colleg...Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places.College students with left-behind experience showed higher aggression levels.To further explore the relationship between left-behind experience and aggression,the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression.One thousand twenty-eight Chinese college students with left-behind experience were recruited,and their aggression levels were assessed.The results showed that there were four categories of left-behind experience:“starting from preschool,frequent contact”(35.5%),“less than 10 years in duration,limited contact”(27.0%),“starting from preschool,over 10 years in duration,limited contact”(10.9%),and“starting from school age,frequent contact”(26.6%).Overall,college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did.Females were less aggressive than males in the“starting from preschool,frequent contact”left-behind situation,while males were less aggressive than females in the“starting from school age,frequent contact”situation.Thesefindings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience.Meanwhile,gender is an important factor in this relationship.展开更多
With the increasing severity of urban traffic congestion and environmental pollution issues,Mobility-as-a-Service(MaaS)has garnered increasing attention as an emerging mode of transportation.Thus,how to motivate users...With the increasing severity of urban traffic congestion and environmental pollution issues,Mobility-as-a-Service(MaaS)has garnered increasing attention as an emerging mode of transportation.Thus,how to motivate users to participate in MaaS has become an important research issue.This study first classified the incentive policies into four aspects:financial incentive policy,non-financial incentive policy,information policy,and convenience policy.Then,through online questionnaires and field interviews,456 sets of data were collected in Beijing,and the data were analyzed by the structural equation model and latent class model.The results show that the four incentive policies are positively correlated with users'participation in MaaS,among which financial incentive policy and information policy have the greatest impact,that is,they can better encourage users by increasing direct financial subsidies and broadening the information about MaaS.In addition,Latent Class Analysis was performed to class different users and it was found that the personal characteristics of users had some influence on willingness to participate in MaaS.Therefore,incentive policies should be designed to consider the needs and characteristics of different user groups to improve their willingness to participate in MaaS.The results can provide theoretical suggestions for the government to promote the widespread application of MaaS in urban transportation.展开更多
Background:There is little literature describing the artificial intelligence(AI)-aided diagnosis of severe pneumonia(SP)subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy.Th...Background:There is little literature describing the artificial intelligence(AI)-aided diagnosis of severe pneumonia(SP)subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy.The aim of our study is to illustrate whether clinical and biological heterogeneity,such as ventilation and gas-exchange,exists among patients with SP using chest computed tomography(CT)-based AI-aided latent class analysis(LCA).Methods:This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1,2015 to May 30,2020.AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population.The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models,and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods.Results:The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes.Patients with subphenotype-1 had milder infections(P<0.001)than patients with subphenotype-2 and had lower 30-day(P<0.001)and 90-day(P<0.001)mortality,and lower in-hospital(P=0.001)and 2-year(P<0.001)mortality.Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume(used to quantify ventilation)and oxygen saturation(used to reflect gas exchange),compared with patients with subphenotype-2.There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes(P<0.001).Compared with patients with subphenotype-2,those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation,and their clinical outcomes were effectively improved after receiving invasive ventilation treatment.Conclusions:A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function.Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.展开更多
Drivers on long interstate bridges often encounter unique challenges,including restricted lane widths,inadequate shoulders,and a lack of clear zones for safe recovery.Studies on understanding the factors that contribu...Drivers on long interstate bridges often encounter unique challenges,including restricted lane widths,inadequate shoulders,and a lack of clear zones for safe recovery.Studies on understanding the factors that contribute to crash severity on such high-risk sections of interstates are limited.This research study applies latent class clustering(LCC)to detect homogeneous clusters while accounting for unobserved heterogeneity in a dataset of 10036 crashes that occurred over a 6-year period(2015–2020)on eight selected bridges.Utilizing the LCC method,the research identifies four optimal clusters in bridge crashes,characterized by attributes such as 04-lane0,06-lane0,0single-vehicle crashes0,and 0 unknown driver0.The association rule mining(ARM)approach is used to identify the important col-lective factors to visible injury(KAB–fatal,severe,and moderate)and property damage only(PDO or no injury).In Cluster 1(4-lane),KAB and PDO crashes differ in collision type and visibility conditions,with rear-end crashes linked to KAB and sideswipe crashes to PDO.Cluster 2(6-lane)shows similar distinctions but lacks specific lighting associations for PDO.In Cluster 3(single-vehicle crashes),KAB involves moderate traffic and low visi-bility,while PDO has lower speed limits and non-dry surfaces.Cluster 4(unknown driver),despite overrepresenting hit-and-run cases,underscores challenges in injury crash data collection in high-volume mobility scenarios.The discussions of the findings on the sever-ity factors in this study are expected to help traffic safety engineers,policymakers,and planners to identify effective safety countermeasures on major elevated sections.展开更多
This study develops a flexible latent class model(LCM)to investigate the electric vehicle(EV)type choice decisions of Halifax residents.It utilizes cross-sectional data from the 2022 Halifax Travel Activity(HaliTRAC)s...This study develops a flexible latent class model(LCM)to investigate the electric vehicle(EV)type choice decisions of Halifax residents.It utilizes cross-sectional data from the 2022 Halifax Travel Activity(HaliTRAC)survey,which includes questions related to EV adoption.This study also analyzes eight attitudes and lifestyle preferences related state-ments using the principal component analysis(PCA)technique,and finally extracts three components labeled as“EV enthusiasts”,“sustainable travellers”,and“remote work arrangement admirers”.This paper explores the heterogeneity between two classes for dif-ferent alternative vehicle type choices,e.g.,battery electric vehicle(BEV),plug-in hybrid electric vehicle(PHEV),hybrid electric vehicle(HEV),and regular internal combustion engine(ICE)vehicle.Based on class membership attributes,class-1 can be identified as those who live in suburban areas,have a large family with high vehicle ownership,and are interested in travelling with their family members,especially with their children and vice-versa for class-2.Results suggest that variables across two classes portray heterogene-ity,e.g.,full-time worker portray positive correlation for class-1 and negative to class-2;high annual household income group(more than$200000)exhibit high propensity to choose BEV in class-2 and vice-versa for class-1.Sustainable travelers emphasize the adverse connection towards regular vehicles,while EV enthusiasts demonstrate a favorable association with embracing any type of EV(e.g.,BEV,PHEV,or HEV).Furthermore,the find-ings from this analysis provide guidance for policy measures such as offering purchase incentives,expanding charging infrastructure,and implementing tax rebates to promote the uptake of EVs among the residents of Halifax.展开更多
Sweden has witnessed an increase in the rates of sexual crimes including rape.Knowledge of who the offenders of these crimes are is therefore of importance for prevention.We aimed to study characteristics of individua...Sweden has witnessed an increase in the rates of sexual crimes including rape.Knowledge of who the offenders of these crimes are is therefore of importance for prevention.We aimed to study characteristics of individuals convicted of rape,aggravated rape,attempted rape or attempted aggravated rape(abbreviated rape+),against a woman≥18years of age,in Sweden.By using information from the Swedish Crime Register,offenders between 15 and 60years old convicted of rapeþbetween 2000 and 2015 were included.Information on substance use disorders,previous criminality and psychiatric disorders were retrieved from Swedish population-based registers,and Latent Class Analysis(LCA)was used to identify classes of rapeþoffenders.A total of 3039 offenders were included in the analysis.A major-ity of them were immigrants(n=1800;59.2%)of which a majority(n=1451;47.7%)were born outside of Sweden.The LCA identified two classes:Class A-low offending class(LOC),and Class B—high offending class(HOC).While offenders in the LOC had low rates of previous criminality,psychiatric disorders and substance use disorders,those included in the HOC had high rates of previous criminality,psychiatric disorders and substance use dis-orders.While HOC may be composed by more“traditional”criminals probably known by the police,the LOC may represent individuals not previously known by the police.These two separated classes,as well as our finding in regard to a majority of the offenders being immi-grants,warrants further studies that take into account the contextual characteristics among these offenders.展开更多
Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid con...Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.展开更多
Background:It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese.Methods:Data were obtained from China Health and Retirement Longitudinal Study(CH...Background:It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese.Methods:Data were obtained from China Health and Retirement Longitudinal Study(CHARLS)2011–2018.We utilized latent class analysis to categorize baseline multimorbidity patterns,Markov multi-state model to explore the impact of multimorbidity characterized by condition counts and multimorbidity patterns on subsequent fall transitions,and Cox proportional hazard models to assess hazard ratios of each transition.Results:A total of 14,244 participants aged 45 years and older were enrolled at baseline.Among these participants,11,956(83.9%)did not have a fall history in the last 2 years,1,054(7.4%)had mild falls,and 1,234(8.7%)had severe falls.Using a multi-state model,10,967 transitions were observed during a total follow-up of 57,094 person-times,6,527 of which had worsening transitions and 4,440 had improving transitions.Among 6,711 multimorbid participants,osteocardiovascular(20.5%),pulmonary-digestive-rheumatic(30.5%),metabolic-cardiovascular(22.9%),and neuropsychiatric-sensory(26.1%)patterns were classified.Multimorbid participants had significantly higher risks of transitions compared with other participants.Among 4 multimorbidity patterns,osteocardiovascular pattern had higher transition risks than other 3 patterns.Conclusions:Multimorbidity,especially the“osteo-cardiovascular pattern”identified in this study,was associated with higher risks of fall transitions among middle-aged and older Chinese.Generally,the effect of multimorbidity is more significant in older adults than in middle-aged adults.Findings from this study provide facts and evidence for fall prevention,and offer implications for clinicians to target on vulnerable population,and for public health policymakers to allocate healthcare resources.展开更多
Introduction:Breast cancer has emerged as the most prevalent cancer among women globally.However,the relationship between individual socioeconomic status(SES)and breast cancer risk remains incompletely understood.Meth...Introduction:Breast cancer has emerged as the most prevalent cancer among women globally.However,the relationship between individual socioeconomic status(SES)and breast cancer risk remains incompletely understood.Methods:This population-based cohort study recruited women aged 30-70 years from Shandong,Hebei,and Jiangsu provinces in China during 2008 and 2018.We developed a composite SES measure through latent class analysis incorporating household income,education level,and health insurance type,stratifying participants into low and high SES groups.Self-perceived SES was evaluated using a Likert scale.We employed Cox proportional hazards regression to estimate hazard ratios(HRs)and 95%confidence intervals(CIs)for the association between SES and breast cancer incidence.Results:Among 62,350 participants followed for an average of 6.1 person-years,we identified 300 incident breast cancer cases.The overall incidence rate was 48.9 per 100,000 person-years.Women with high SES demonstrated significantly elevated breast cancer risk compared to those with low SES(HR=1.42,95%CI:1.05-1.92).Self-perceived SES appeared to modify this relationship,with increased breast cancer risk observed among women categorized as both objectively and subjectively low SES.Conclusions:These findings underscore the need for SES-specific approaches to breast cancer screening programs and targeted health education initiatives.展开更多
Background Schistosoma haematobium and S.mansoni are endemic in Madagascar,but reliable diagnostic tools are often lacking,contributing to exacerbate transmission and morbidity.This study evaluated the diagnostic accu...Background Schistosoma haematobium and S.mansoni are endemic in Madagascar,but reliable diagnostic tools are often lacking,contributing to exacerbate transmission and morbidity.This study evaluated the diagnostic accuracy of three tests for schistosome infection in Malagasy adults from areas of medium to high endemicity.Methods This cross-sectional study enrolled adults from three primary health care centres in Madagascar.Urine and blood samples were tested for schistosome infection using polymerase chain reaction(PCR),up-converting reporter particle lateral flow for the circulating anodic antigen(UCP-LF CAA),and point-of-care circulating cathodic antigen(POC-CCA)tests.Bayesian latent class models were used to assess diagnostic accuracies and disease prevalence.Results Of 1339 participants,461 were from S.haematobium and 878 from S.mansoni endemic areas.Test detection rates were 52%(POC-CCA),60%(UCP-LF CAA),and 66%(PCR)in the S.haematobium area,and 54%,55%,and 59%respectively in the S.mansoni area.For S.haematobium,PCR and UCP-LF CAA showed high sensitivity(Se,median 95.2%and 87.8%)but moderate specificity(Sp,60.3%and 66.2%),while POC-CCA performed moderately(Se:64.5%;Sp:59.6%).For S.mansoni,PCR and POC-CCA demonstrated high diagnostic accuracy(Se>90%,Sp>80%),while UCP-LF CAA showed good sensitivity(79.9%)but moderate specificity(69.7%).Conclusions While population-level prevalence estimates were similar across tests,individual-level agreement was only low to moderate.Our findings suggest that optimal diagnostic strategies should be tailored to specific endemic settings,continued development of accurate diagnostics suitable for highly endemic settings remains a priority.展开更多
Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environ...Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environment.This study develops an assessment framework for evaluating walking satisfaction on sidewalks in commercial districts in Japan according to the built environment and personal attributes.Questionnaire surveys were used to collect data from 963 Japanese residents’impact ratings of built environment variables.Six factors were extracted following exploratory factor analysis.Second-order confirmatory factor analysis was then applied to further explore the relationship between observed variables and latent factors.Latent class analysis was employed to classify the population,and personal attributes served as covariates in the multinomial logistic regression analysis to explore how these attributes affected the latent classes.The results indicated that visual impression,spatial richness,accessibility,comfort,diversity,and security influence pedestrian walking satisfaction on sidewalks in commercial districts.Travel purpose and travel method are important indicators that influence the latent classes of the population.The result presented herein can guide policy makers to optimize the construction of walkable urban environments and enact policies based on the factors and populations that are best suited to urban development.展开更多
This paper presents a study that explored the behavioral heterogeneity of changes in peo-ple’s information and communications technology(ICT)usage and travel patterns at the end of the pandemic.A quasi-longitudinal a...This paper presents a study that explored the behavioral heterogeneity of changes in peo-ple’s information and communications technology(ICT)usage and travel patterns at the end of the pandemic.A quasi-longitudinal approach was employed to collect data from Florida residents,capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021.Utilizing the latent class analysis(LCA)approach to identify subgroups based on the online activity durations and trip frequencies,four distinct classes were identified.A little more than one third(35%)of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies.About 33%of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities.About 16%of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities.Another 16%of the respon-dents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities.These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities.The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.展开更多
基金funding from the China National Key Research and Development Program(No.2023YFC3603104)the National Natural Science Foundation of China(Nos.82472243 and 82272180)+6 种基金the Fundamental Research Funds for the Central Universities(No.226-2025-00024)the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LHDMD24H150001)the Key Research&Development Project of Zhejiang Province(No.2024C03240)a collaborative scientific project co-established by the Science and Technology Department of the National Administration of Traditional Chinese Medicine and the Zhejiang Provincial Administration of Traditional Chinese Medicine(No.GZY-ZJ-KJ-24082)he General Health Science and Technology Program of Zhejiang Province(No.2024KY1099)the Project of Zhejiang University Longquan Innovation Center(No.ZJDXLQCXZCJBGS2024016)Wu Jieping Medical Foundation Special Research Grant(No.320.6750.2024-23-07).
文摘Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.
文摘Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.
基金supported by the Hospital-level Nursing Research Project of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine(xhhlcx2023-017)the third period of the 14th Five-Year nursing talent project of Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine(Xhlxm014)the Ministry of Education of Humanities and Social Science Project(23YJC630002)and High-level local university construction project founded by Shanghai Municipal Education Commission.
文摘Objectives:To identify the subgroups of self-reported outcomes and associated factors among breast cancer patients undergoing surgery and chemotherapy.Methods:A cross-sectional study was conducted between January and November 2021.We recruited patients from two tertiary hospitals in Shanghai,China,using convenience sampling during their hospitalization.Patients were assessed using a questionnaire that included sociodemographic and clinical characteristics,the Patient Reported Outcomes Measurement Information System profile-29(PROMIS-29),and the PROMIS-cognitive function short form 4a.Latent class analysis was performed to examine possible classes regarding self-reported outcomes.Multiple logistic regression analysis was used to determine the associated factors.Analysis of variance(ANOVA)was conducted for symptoms across the different classes.Results:A total of 640 patients participated in this study.The findings revealed three subgroups in terms of self-reported outcomes among breast cancer patients undergoing surgery and chemotherapy:low physical-social-cognitive function,high physical-low cognitive function,and high physical-socialcognitive function.Multivariable logistic regression analysis showed that age(≥60 years old),menopause,the third chemotherapy cycle,undergoing simple mastectomy and breast reconstruction,duration of disease 3-12 months,stageⅢ/Ⅳcancer,and severe pain were associated factors of the functional decline groups.Besides,significant differences in depression and sleep disorders were observed among the three groups.Conclusions:Breast cancer patients receiving surgery and chemotherapy can be divided into three subgroups.Aging,menopause,chemotherapy cycle,surgery type,duration and stage of disease,and severe pain affected the functional decline groups.Consequently,healthcare professionals should make tailored interventions to address the specific functional rehabilitation and symptom relief needs.
文摘Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.
基金This paper is supported by the National Key Research and Development Program of China(2019YFF0301400)the National Natural Science Foundation of China(61671031,61722102,and 61961146005).
文摘In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.
基金supported in part by the National Key R&D Program of China(Grant No.2017YFA0604804)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA20020402)the National Natural Science Foundation of China(Grant NO.42171079)。
文摘Critical transitions in ecosystems may imply risks of unexpected collapse under climate changes,especially vegetation often responds sensitively to climate change.The type of vegetation ecosystem states could present alternative stable states,and its type could signal the critical transitions at tipping points because of changed climate or other drivers.This study analyzed the distribution of four key vegetation ecosystem types:desert,grassland,forest-steppe ecotone and forest,in Tibetan Plateau in China,using the latent class analysis method based on remote sensing data and climate data.This study analyzed the impacts of three key climate factors,precipitation,temperature,and sunshine duration,on the vegetation states,and calculated the critical transition tipping point of potential changes in vegetation type in Tibetan Plateau with the logistic regression model.The studied results showed that climatic factors greatly affect the vegetation states and vulnerability of the Tibetan Plateau.In comparison with temperature and sunshine duration,precipitation shows more obvious impact on differentiations of the vegetations status probability.The precipitation tipping point for desert and grassland transition is averagely 48.0 mm/month,70.7 mm/month for grassland and forest-steppe ecotone,and 115.0 mm/month for forest-steppe ecotone and forest.Both temperature and sunshine duration only show different probability change between vegetation and non-vegetation type,but produce opposite impacts.In Tibetan Plateau,the transition tipping points of vegetation and nonvegetation are about 12.1°C/month and 173.6 h/month for the temperature and sunshine duration,respectively.Further,vulnerability maps calculated with the logistic regression results presented the distribution of vulnerability of Tibetan Plateau key ecosystems.The vulnerability of the typical ecosystems in the Tibetan Plateau is low in the southeast and is high in the northwest.The meteorological factors affect tree cover as well as the transition probability that occurs in different vegetation states.This study can provide reference for local government agencies to formulate regional development strategies and environmental protection laws and regulations.
基金supported by the French Federation of Table Tennis.
文摘Purpose:The purposes of this study were to examine the trajectories of athlete burnout across a 2-month period characterized by high physical,psychological,and social demands to explore(1)whether several subgroups of athletes representing distinct burnout trajectories emerged from the analyses and(2)whether athlete burnout symptoms(reduced accomplishment,sport devaluation,and exhaustion)developed in tandem or whether some burnout dimensions predicted downstream changes in other dimensions(causal ordering model).Methods:One hundred and fifty-nine table tennis players in intensive training centers completed a self-reported athlete burnout measure across 3 time points within a 2-month period characterized by high demands.Data were analyzed through latent class growth analysis.Results:Results of latent class growth analysis showed 3 distinct trajectories for each athlete burnout dimension,indicating not only linear or quadratic change but also stability in longitudinal athlete burnout perceptions.Results also suggested that the 3 dimensions of athlete burnout did not develop in tandem.Rather,the likelihood of belonging to particular emerging trajectories of sport devaluation and physical/emotional exhaustion was significantly influenced by the athletes’perception of reduced accomplishment assessed at Time 1.Thus,reduced accomplishment predicted downstream changes in the 2 other athlete burnout dimensions.Conclusion:As a whole,these results highlighted that the multinomial heterogeneity in longitudinal athlete burnout symptoms needs to be accounted for in future research.
基金supported by the Yonsei University Research Fund of 2021(2021-22-0060).
文摘Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.
基金supported by the National Natural Science Foundation of China(Grant No.31800929)Fundamental Research Funds for the Central Universities(Grant No.2020NTSS42).
文摘Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places.College students with left-behind experience showed higher aggression levels.To further explore the relationship between left-behind experience and aggression,the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression.One thousand twenty-eight Chinese college students with left-behind experience were recruited,and their aggression levels were assessed.The results showed that there were four categories of left-behind experience:“starting from preschool,frequent contact”(35.5%),“less than 10 years in duration,limited contact”(27.0%),“starting from preschool,over 10 years in duration,limited contact”(10.9%),and“starting from school age,frequent contact”(26.6%).Overall,college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did.Females were less aggressive than males in the“starting from preschool,frequent contact”left-behind situation,while males were less aggressive than females in the“starting from school age,frequent contact”situation.Thesefindings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience.Meanwhile,gender is an important factor in this relationship.
基金sponsored by The National Natural Science Foundation of China(Grant No.71971020).
文摘With the increasing severity of urban traffic congestion and environmental pollution issues,Mobility-as-a-Service(MaaS)has garnered increasing attention as an emerging mode of transportation.Thus,how to motivate users to participate in MaaS has become an important research issue.This study first classified the incentive policies into four aspects:financial incentive policy,non-financial incentive policy,information policy,and convenience policy.Then,through online questionnaires and field interviews,456 sets of data were collected in Beijing,and the data were analyzed by the structural equation model and latent class model.The results show that the four incentive policies are positively correlated with users'participation in MaaS,among which financial incentive policy and information policy have the greatest impact,that is,they can better encourage users by increasing direct financial subsidies and broadening the information about MaaS.In addition,Latent Class Analysis was performed to class different users and it was found that the personal characteristics of users had some influence on willingness to participate in MaaS.Therefore,incentive policies should be designed to consider the needs and characteristics of different user groups to improve their willingness to participate in MaaS.The results can provide theoretical suggestions for the government to promote the widespread application of MaaS in urban transportation.
基金supported by the National Natural Science Foundation of China(Nos.82172138 and 81873947).
文摘Background:There is little literature describing the artificial intelligence(AI)-aided diagnosis of severe pneumonia(SP)subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy.The aim of our study is to illustrate whether clinical and biological heterogeneity,such as ventilation and gas-exchange,exists among patients with SP using chest computed tomography(CT)-based AI-aided latent class analysis(LCA).Methods:This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1,2015 to May 30,2020.AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population.The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models,and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods.Results:The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes.Patients with subphenotype-1 had milder infections(P<0.001)than patients with subphenotype-2 and had lower 30-day(P<0.001)and 90-day(P<0.001)mortality,and lower in-hospital(P=0.001)and 2-year(P<0.001)mortality.Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume(used to quantify ventilation)and oxygen saturation(used to reflect gas exchange),compared with patients with subphenotype-2.There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes(P<0.001).Compared with patients with subphenotype-2,those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation,and their clinical outcomes were effectively improved after receiving invasive ventilation treatment.Conclusions:A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function.Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
基金funded by the Louisiana Department of Transportation(DOTD)and Development and the Louisiana Transportation Research Center(LTRC),Grant number DOTLT1000341.
文摘Drivers on long interstate bridges often encounter unique challenges,including restricted lane widths,inadequate shoulders,and a lack of clear zones for safe recovery.Studies on understanding the factors that contribute to crash severity on such high-risk sections of interstates are limited.This research study applies latent class clustering(LCC)to detect homogeneous clusters while accounting for unobserved heterogeneity in a dataset of 10036 crashes that occurred over a 6-year period(2015–2020)on eight selected bridges.Utilizing the LCC method,the research identifies four optimal clusters in bridge crashes,characterized by attributes such as 04-lane0,06-lane0,0single-vehicle crashes0,and 0 unknown driver0.The association rule mining(ARM)approach is used to identify the important col-lective factors to visible injury(KAB–fatal,severe,and moderate)and property damage only(PDO or no injury).In Cluster 1(4-lane),KAB and PDO crashes differ in collision type and visibility conditions,with rear-end crashes linked to KAB and sideswipe crashes to PDO.Cluster 2(6-lane)shows similar distinctions but lacks specific lighting associations for PDO.In Cluster 3(single-vehicle crashes),KAB involves moderate traffic and low visi-bility,while PDO has lower speed limits and non-dry surfaces.Cluster 4(unknown driver),despite overrepresenting hit-and-run cases,underscores challenges in injury crash data collection in high-volume mobility scenarios.The discussions of the findings on the sever-ity factors in this study are expected to help traffic safety engineers,policymakers,and planners to identify effective safety countermeasures on major elevated sections.
文摘This study develops a flexible latent class model(LCM)to investigate the electric vehicle(EV)type choice decisions of Halifax residents.It utilizes cross-sectional data from the 2022 Halifax Travel Activity(HaliTRAC)survey,which includes questions related to EV adoption.This study also analyzes eight attitudes and lifestyle preferences related state-ments using the principal component analysis(PCA)technique,and finally extracts three components labeled as“EV enthusiasts”,“sustainable travellers”,and“remote work arrangement admirers”.This paper explores the heterogeneity between two classes for dif-ferent alternative vehicle type choices,e.g.,battery electric vehicle(BEV),plug-in hybrid electric vehicle(PHEV),hybrid electric vehicle(HEV),and regular internal combustion engine(ICE)vehicle.Based on class membership attributes,class-1 can be identified as those who live in suburban areas,have a large family with high vehicle ownership,and are interested in travelling with their family members,especially with their children and vice-versa for class-2.Results suggest that variables across two classes portray heterogene-ity,e.g.,full-time worker portray positive correlation for class-1 and negative to class-2;high annual household income group(more than$200000)exhibit high propensity to choose BEV in class-2 and vice-versa for class-1.Sustainable travelers emphasize the adverse connection towards regular vehicles,while EV enthusiasts demonstrate a favorable association with embracing any type of EV(e.g.,BEV,PHEV,or HEV).Furthermore,the find-ings from this analysis provide guidance for policy measures such as offering purchase incentives,expanding charging infrastructure,and implementing tax rebates to promote the uptake of EVs among the residents of Halifax.
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme[grant number 787592].
文摘Sweden has witnessed an increase in the rates of sexual crimes including rape.Knowledge of who the offenders of these crimes are is therefore of importance for prevention.We aimed to study characteristics of individuals convicted of rape,aggravated rape,attempted rape or attempted aggravated rape(abbreviated rape+),against a woman≥18years of age,in Sweden.By using information from the Swedish Crime Register,offenders between 15 and 60years old convicted of rapeþbetween 2000 and 2015 were included.Information on substance use disorders,previous criminality and psychiatric disorders were retrieved from Swedish population-based registers,and Latent Class Analysis(LCA)was used to identify classes of rapeþoffenders.A total of 3039 offenders were included in the analysis.A major-ity of them were immigrants(n=1800;59.2%)of which a majority(n=1451;47.7%)were born outside of Sweden.The LCA identified two classes:Class A-low offending class(LOC),and Class B—high offending class(HOC).While offenders in the LOC had low rates of previous criminality,psychiatric disorders and substance use disorders,those included in the HOC had high rates of previous criminality,psychiatric disorders and substance use dis-orders.While HOC may be composed by more“traditional”criminals probably known by the police,the LOC may represent individuals not previously known by the police.These two separated classes,as well as our finding in regard to a majority of the offenders being immi-grants,warrants further studies that take into account the contextual characteristics among these offenders.
基金funded by University of Transport and Commu-nications (UTC) (Grant No.T2019-CT-06TD).
文摘Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.
基金funded by the National Key R&D Program of China(2023YFB4603200 and 2023YFC3606400)National Natural Science Foundation of China(72374013)Capital’s Funds for Health Improvement and Research(CFH 2024-1G-3014).
文摘Background:It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese.Methods:Data were obtained from China Health and Retirement Longitudinal Study(CHARLS)2011–2018.We utilized latent class analysis to categorize baseline multimorbidity patterns,Markov multi-state model to explore the impact of multimorbidity characterized by condition counts and multimorbidity patterns on subsequent fall transitions,and Cox proportional hazard models to assess hazard ratios of each transition.Results:A total of 14,244 participants aged 45 years and older were enrolled at baseline.Among these participants,11,956(83.9%)did not have a fall history in the last 2 years,1,054(7.4%)had mild falls,and 1,234(8.7%)had severe falls.Using a multi-state model,10,967 transitions were observed during a total follow-up of 57,094 person-times,6,527 of which had worsening transitions and 4,440 had improving transitions.Among 6,711 multimorbid participants,osteocardiovascular(20.5%),pulmonary-digestive-rheumatic(30.5%),metabolic-cardiovascular(22.9%),and neuropsychiatric-sensory(26.1%)patterns were classified.Multimorbid participants had significantly higher risks of transitions compared with other participants.Among 4 multimorbidity patterns,osteocardiovascular pattern had higher transition risks than other 3 patterns.Conclusions:Multimorbidity,especially the“osteo-cardiovascular pattern”identified in this study,was associated with higher risks of fall transitions among middle-aged and older Chinese.Generally,the effect of multimorbidity is more significant in older adults than in middle-aged adults.Findings from this study provide facts and evidence for fall prevention,and offer implications for clinicians to target on vulnerable population,and for public health policymakers to allocate healthcare resources.
基金Supported by the National Key R&D Program of China(2016YFC0901300,2016YFC0901301)IMICAMS Youth Talent Development Fund under Number 2024YT02,and the Strategic Research and Consultancy Project of the Chinese Academy of Engineering(2023-JB-11).
文摘Introduction:Breast cancer has emerged as the most prevalent cancer among women globally.However,the relationship between individual socioeconomic status(SES)and breast cancer risk remains incompletely understood.Methods:This population-based cohort study recruited women aged 30-70 years from Shandong,Hebei,and Jiangsu provinces in China during 2008 and 2018.We developed a composite SES measure through latent class analysis incorporating household income,education level,and health insurance type,stratifying participants into low and high SES groups.Self-perceived SES was evaluated using a Likert scale.We employed Cox proportional hazards regression to estimate hazard ratios(HRs)and 95%confidence intervals(CIs)for the association between SES and breast cancer incidence.Results:Among 62,350 participants followed for an average of 6.1 person-years,we identified 300 incident breast cancer cases.The overall incidence rate was 48.9 per 100,000 person-years.Women with high SES demonstrated significantly elevated breast cancer risk compared to those with low SES(HR=1.42,95%CI:1.05-1.92).Self-perceived SES appeared to modify this relationship,with increased breast cancer risk observed among women categorized as both objectively and subjectively low SES.Conclusions:These findings underscore the need for SES-specific approaches to breast cancer screening programs and targeted health education initiatives.
基金Open Access funding enabled and organized by Projekt DEALThis work was supported by the German Center for Infection Research(Grant number TI 03.907_00).
文摘Background Schistosoma haematobium and S.mansoni are endemic in Madagascar,but reliable diagnostic tools are often lacking,contributing to exacerbate transmission and morbidity.This study evaluated the diagnostic accuracy of three tests for schistosome infection in Malagasy adults from areas of medium to high endemicity.Methods This cross-sectional study enrolled adults from three primary health care centres in Madagascar.Urine and blood samples were tested for schistosome infection using polymerase chain reaction(PCR),up-converting reporter particle lateral flow for the circulating anodic antigen(UCP-LF CAA),and point-of-care circulating cathodic antigen(POC-CCA)tests.Bayesian latent class models were used to assess diagnostic accuracies and disease prevalence.Results Of 1339 participants,461 were from S.haematobium and 878 from S.mansoni endemic areas.Test detection rates were 52%(POC-CCA),60%(UCP-LF CAA),and 66%(PCR)in the S.haematobium area,and 54%,55%,and 59%respectively in the S.mansoni area.For S.haematobium,PCR and UCP-LF CAA showed high sensitivity(Se,median 95.2%and 87.8%)but moderate specificity(Sp,60.3%and 66.2%),while POC-CCA performed moderately(Se:64.5%;Sp:59.6%).For S.mansoni,PCR and POC-CCA demonstrated high diagnostic accuracy(Se>90%,Sp>80%),while UCP-LF CAA showed good sensitivity(79.9%)but moderate specificity(69.7%).Conclusions While population-level prevalence estimates were similar across tests,individual-level agreement was only low to moderate.Our findings suggest that optimal diagnostic strategies should be tailored to specific endemic settings,continued development of accurate diagnostics suitable for highly endemic settings remains a priority.
基金funding support from the JST SPRING(Grant No.JPMJSP2128).
文摘Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environment.This study develops an assessment framework for evaluating walking satisfaction on sidewalks in commercial districts in Japan according to the built environment and personal attributes.Questionnaire surveys were used to collect data from 963 Japanese residents’impact ratings of built environment variables.Six factors were extracted following exploratory factor analysis.Second-order confirmatory factor analysis was then applied to further explore the relationship between observed variables and latent factors.Latent class analysis was employed to classify the population,and personal attributes served as covariates in the multinomial logistic regression analysis to explore how these attributes affected the latent classes.The results indicated that visual impression,spatial richness,accessibility,comfort,diversity,and security influence pedestrian walking satisfaction on sidewalks in commercial districts.Travel purpose and travel method are important indicators that influence the latent classes of the population.The result presented herein can guide policy makers to optimize the construction of walkable urban environments and enact policies based on the factors and populations that are best suited to urban development.
基金sponsored by the Florida Department of Transportation(BDV29977-59).
文摘This paper presents a study that explored the behavioral heterogeneity of changes in peo-ple’s information and communications technology(ICT)usage and travel patterns at the end of the pandemic.A quasi-longitudinal approach was employed to collect data from Florida residents,capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021.Utilizing the latent class analysis(LCA)approach to identify subgroups based on the online activity durations and trip frequencies,four distinct classes were identified.A little more than one third(35%)of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies.About 33%of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities.About 16%of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities.Another 16%of the respon-dents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities.These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities.The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.
